evalml.pipelines.utils.make_pipeline¶
-
evalml.pipelines.utils.make_pipeline(X, y, estimator, problem_type, parameters=None, custom_hyperparameters=None, sampler_name=None)[source]¶ - Given input data, target data, an estimator class and the problem type,
generates a pipeline class with a preprocessing chain which was recommended based on the inputs. The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type.
- Parameters
X (pd.DataFrame) – The input data of shape [n_samples, n_features]
y (pd.Series) – The target data of length [n_samples]
estimator (Estimator) – Estimator for pipeline
problem_type (ProblemTypes or str) – Problem type for pipeline to generate
parameters (dict) – Dictionary with component names as keys and dictionary of that component’s parameters as values. An empty dictionary or None implies using all default values for component parameters.
custom_hyperparameters (dictionary) – Dictionary of custom hyperparameters, with component name as key and dictionary of parameters as the value
sampler_name – The name of the sampler component to add to the pipeline. Only used in classification problems. Defaults to None